Computational requirements of contemporary brain science research typically exceed financial and resource management limits of individual investigator laboratories. Many brain science research projects require analysis of large data sets with advanced statistical methods and anatomical reconstruction techniques. These methods require high speed computational and graphics engines operating in a multiple processor environments equipped with large capacity, high speed storage devices. An ongoing limitation in the Brown brain science effort at understanding neural processing is the lack of a contemporary and readily accessible high-speed computational resource. We plan to replace an existing, but 5 year old and now outmoded, central computational resource that has outdated graphic processing units (GPU) and central processing units (CPU) and and limited storage that will serve the computational needs of a core group of brain science investigators at Brown without compromising individual access to stand-alone workstations. The requested computation equipment comprises 13 GPU nodes (total of 52 cores), 12 CPU nodes (288 cores) and 1.2 petabytes of disk storage, which will serve the needs of the assembled brain science researchers. The equipment will become integrated into Brown's high performance Compute Cluster, which has system software that automatically balances GPU and CPU usage, thereby ensuring maximum access to the computational resource for all users. Intensive 3D graphics are off- loaded either to GPUs or to client workstations, thereby further reducing the central computational load. Commercial or open-source software with an open operating environment will be used for analysis using standard and novel statistical and machine learning approaches to assess significance of large data sets. This proposal details the architecture and benefits of a contemporary computational resource for the major and minor users, and more generally the Brown brain science community. The resource was designed to fill immediate and near-term computational and storage needs of a core group of Brown brain scientists. The system can be readily expansion as needs, either computational, storage, or new users, arise. Expansion of the existing core investigators group can occur easily since the computational power or storage capacity of the system can be readily enhanced at relatively low cost. The flexible nature of the system will serve a variety of research needs of the Brown brain science community. The computational resource is expected to bring together researchers at Brown working on the common problem of neural processing.